Linking Discrete Dimensions to Enhance Dimensional Analysis

ABSTRACT

Not all facts in a data warehouse are described by the same set of dimensions. However, there can be associations between the data dimensions and other dimensions. By maintaining a set of relationships that are capable of linking the dimensional keys used in existing data to the keys of an associated dimension, a data transformation can be constructed that summarizes by the original and by the associated dimensions in feeds in an analytical data mart (cube) that includes all the dimensions. This cube can then be consolidated and analyzed in a slice-and-dice fashion as though all the dimensions were independent. Data transformed in this manner can be analyzed alongside data from a source that is keyed by all of the dimensions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.12/409,180, filed on Mar. 23, 2009, entitled “Linking DiscreteDimensions to Enhance Dimensional Analysis,” which claims the benefitunder 35 U.S.C. §119(e) to U.S. Provisional Application No. 61/038,904,filed Mar. 24, 2008, and entitled “Linking Discrete Dimensions toEnhance Dimensional Analysis.” U.S. patent application Ser. No.12/409,180 and U.S. Provisional Application No. 61/038,904 are assignedto the assignee of the present application. The subject matter disclosedin U.S. patent application Ser. No. 12/409,180 and U.S. ProvisionalApplication No. 61/038,904 is hereby incorporated by reference into thepresent disclosure as if fully set forth herein.

TECHNICAL FIELD

One exemplary aspect of this invention generally relates to the field ofdimensional data warehouses and data marts. More specifically, anexemplary embodiment relates to a database structure utilizing amultidimensional cube.

BACKGROUND

Data Warehouses are commonly designed using Dimensional Modeling. Such aData Warehouse is known as a Dimensional Data Warehouse. Typically datain a Dimensional Data Warehouse is stored in fact tables that connect todimension tables in a design known as star-schema. Sourced from the basedata in star-schemas, multidimensional structures called OnlineAnalytical Processing (OLAP) cubes are often built for analysis andreporting purposes.

The base data in a Dimensional Data Warehouse is stored in a Relationaldatabase. Relational databases use a collection of relations e.g.tables, to define a relational model to which the relational databaseconforms. In relational databases, the data is typically accessedthrough the use of a Structured Query Language (SQL) type query.

The OLAP cubes may be implemented in a relational database. This isknown as Relational OLAP (ROLAP), or in a multidimensional databaseenvironment, as Multidimensional OLAP (MOLAP).

In OLAP, the structure of the database allows rapid processing of thedata such that queries can be answered quickly with reduced processorburden. This is facilitated by the use of a data cube which representsthe dimensions of data available. For example, “Sales” could be viewedin the dimensions of Item, Product, Geography, Time, or some additionaldimension. In this case, “Sales” is referred to as the measure attributeof the data cube and the other dimensions are referred to as the featureattribute. A database creator can also define hierarchies and levelswithin a dimension, such as cosmetics-skin care products-lotion, with anassociated hierarchy within the dimension.

SUMMARY

Understanding a product's repeatable seasonal pattern is critical tointerpreting historical sales and subsequently generating an accurateforecast of future demand. Achieving optimized replenishment requiresaccurate seasonal profiles coupled with strategic analysis. In order tofollow sound forecasting practices, one should have a clear-cutrepresentation of the selling curve for a time period, for a product andfor a location. Without such profiles, a forecast could confuse theelevated levels of business that might occur during the holiday season,for example, with a high growth trend, and then accelerate the receiptof products to unrealistic levels in January.

One exemplary embodiment of the invention utilizes these advancedtechniques and computing power to produce profiles at low levels of theproduct hierarchy, on, for example, a location-by-location basis andwith indications of individual location/product time curves. Thisexemplary embodiment can also automatically revise profiles in-seasonusing new performance data. The numerical techniques used allow accurateprofiling even for low-volume and low frequency items, all implementedusing automation for review of tens to hundreds of millions oflocation/SKU sales combinations.

Another exemplary aspect of this invention generally relates to thefield of dimensional data warehouses, and specifically to the challengesof analyzing data of different, but related, dimensionalities. Not allfacts in a data warehouse are described by the same set of dimensions.However, there can be associations between the dimensions associatedwith the database and other dimensions. A difficulty exists in how topresent aggregated data in an OLAP cube with a consistent set ofdimensions that can incorporate data with different definingdimensionality.

Current solutions involve assigning associated dimension members in thefeed of information coming to the data warehouse so that the data to beanalyzed together has a consistent set of dimensionalities. However,there are several drawbacks associated with this approach. For example,additional processing on large volumes of data needs to be undertaken.Furthermore, the analytical needs must be anticipated at the time ofpreparing the data feeds or, if identified later, the data feeds mayneed to be re-written and previously-loaded data re-organized to assignadditional dimension keys. Additionally, the additional dimension keysand data records increase the size of typically what are the largesttables in the data warehouse.

By maintaining a set of relationships that are capable of linking thedimensional keys used in the existing data to the keys of the associateddimension, a data transformation can be constructed that summarizes bythe original and by the associated dimensions in feeds to an OLAP cubethat includes all the dimensions. This cube can then be consolidated andanalyzed in a slice-and-dice fashion as though all the dimensions wereindependent. Data transformed in this manner can be analyzed alongsidedata from a source that is keyed by all of the dimensions.

Thus, for an end user, this solution is capable of adding value to anexisting dimensional data warehouse. Therefore, analytical needs thatwere not previously possible can be accommodated without prohibitivereloading, rebuilding, and without a corresponding increase in size ofthe data tables in the data warehouse. For a software vendor, it alsoadvantageously provides a growth path as additional applicationcomponents are developed that avoids rework of existing features and aneasy migration path for prospective customers.

One exemplary OLAP solution utilizes a multi-cube approach, in which themulti-cube comprises a series of small pre-calculated cubes. Thesepre-calculated cubes are sometimes referred to as a hypercubes. Thisstructure facilitates the slice-and-dice of information thereforeproviding the ability of a multidimensional analysis through the use ofthe data cube and can be used with the features of this invention.

For example, OLAP can be the basis of an analysis tool that allows quickand ready analysis of questions that involve, for example, a timeline.Other environments in which OLAP has advantages are if the questionsrelate to geographical areas, various product lines, categories and/orchannels. For example, as will be discussed herein, OLAP can allow auser to align and analyze data relating to the number of products sold,sales location, and season within a given planning cycle, or in general,any metric.

Accordingly, exemplary aspects of the invention relate to a databasestructure utilizing an OLAP cube.

Still further aspects of the invention relate to combining dimensionsand data aggregation in a database environment.

Still further aspects of the invention relate to a dimensioned datawarehouse that comprises original data with dimension keys and arelationship that links the dimension keys to new dimension keys togenerate a new multidimensional cube.

Even further aspects of the invention relating to utilizing rules to tagdata with a dimension code.

Still further aspects of the invention relate to utilizing a template toselect a rule set that tags data with dimension codes.

Additional aspects of the invention relate to building an analyticalcube with additional dimensions that can be used for one or more of dataanalysis, feedback for demand chain management, reporting, or the like.

Still further aspects of the invention relate to establishing one ormore relationships between existing data that includes dimensional keysand one or more new dimensional keys.

Aspects of the invention also relate to collecting base data, analyzingand tagging the base data with keys corresponding to a dimension basedon one or more rules, aggregating by the original and new keys,constructing a multidimensional cube with additional dimensions for dataanalysis, and slicing-and-dicing of the data. The base data can relateto and represent one or more of products and services, such as a coat,cold medicine, hotel room, winterizing car service, or the like.

Even further aspects of the invention relate to providing a userinterface comprising dimensioned data, where one of the dimensions maynot have been loaded with the data.

Another aspect of the invention provides an output, such as a userinterface, with data representing one or more of a product and servicewith one or more additional dimensions.

Still further aspects of the invention relate to outputting informationrepresenting one or more of a product and service with one or moreadditional dimensions.

Still further aspects of the invention relate to outputting informationrepresenting one or more of a product and service with one or moreadditional relationships.

The preceding is a simplified summary of the summary of the invention toprovide an understanding of some aspects thereof. This summary isneither an exhaustive nor extensive overview of the invention and itsvarious embodiments. It is intended neither to identify key or criticalelements of the invention nor to delineate the scope of the invention,but to present selected concepts of the invention in a simplified formas an introduction to the more detailed description presented below. Aswill be appreciated, other embodiments of the invention are possibleutilizing, alone or in combination, one or more of the features as setforth above or described in detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary data analysis system according to thisinvention;

FIG. 2 illustrates an exemplary relationship between primary dimensionsaccording to this invention;

FIG. 3 is a flowchart outlining the exemplary method of linking discretedimensions to enhanced dimensional analysis according to this invention;and

FIG. 4 illustrates represents an exemplary seasonality summary accordingto this invention.

DETAILED DESCRIPTION

The term “automatic” and variations thereof, as used herein, refers toany process or operation done without material human input when theprocess or operation is performed. However, a process or operation canbe automatic even if performance of the process or operation uses humaninput, whether material or immaterial, received before performance ofthe process or operation. Human input is deemed to be material if suchinput influences how the process or operation will be performed. Humaninput that consents to the performance of the process or operation isnot deemed to be “material.”

The term “computer-readable medium” as used herein refers to anytangible storage and/or transmission medium that participates inproviding instructions to a processor for execution. Such a medium maytake many forms, including but not limited to, non-volatile media,volatile media and transmission media. Non-volatile media includes, forexample, NVRAM, or magnetic and/or optical disks. Volatile mediaincludes dynamic memory, such as main memory. Common forms ofcomputer-readable media include, for example, a floppy disk, a flexibledisk, hard disk, magnetic tape, or any other magnetic medium,magneto-optical medium, a CD-ROM, any other optical medium, punch cards,paper tape, any other physical medium with patterns of holes ordeformation, a RAM, a PROM, and EPROM, a FLASH-EPROM, a solid statemedium like a memory card, any other memory chip or cartridge, a carrierwave as described hereinafter, or any other medium from which a computercan read. A digital file attachment to e-mail or other self-containedinformation archive or set of archives is considered a distributionmedium equivalent to a tangible storage medium. When thecomputer-readable media is configured as a database, it is to beunderstood that the database may be any type of database, such asrelational, hierarchical, object-oriented, and/or the like. Accordingly,the invention is considered to include a tangible storage medium ordistribution medium and prior art-recognized equivalents and successormedia, in which the software implementations of the present inventionare stored.

The terms “determine,” “calculate” and “compute,” and variationsthereof, as used herein, are used interchangeably and include any typeof methodology, process, mathematical operation or technique.

The term “module” as used herein refers to any known or later developedhardware, software, firmware, artificial intelligence, fuzzy logic, orcombination of hardware and software that is capable of performing thefunctionality associated with that element. Also, while the invention isdescribed in terms of exemplary embodiments, it should be appreciatedthat individual aspects of the invention can be separately claimed.

FIG. 1 illustrates an exemplary data analysis system. The data analysissystem comprises one or more information sources 20, a data warehouse50, and the data analysis system 1, linked by one or more links 5 andnetworks 10.

The data analysis system 1 comprises an analysis module 110, a rulesmodule 120, a data tagging module 130, a multidimensional cube(s) 140,storage 150, a processor 160, 110 interface 170, a plug-n-play dimensionmodule 180, a user interface module 190 and a data translation module195.

The data analysis system 1 is at least capable of outputting one or moreof feedback instructions for demand chain management, reports to, forexample, a printer and/or display 210, and a data structure, such as amultidimensional cube, which can then be stored on storage device 220.

In operation, base data is collected from one or more informationsources 20. This information is forwarded, via one or more of links 5and networks 10, to the data warehouse 50. The data warehouse 50 storesthe base data in, for example, the format d1, d2, d3, m1, m2, m3, whered1, d2 and d3 are dimension keys and m1, m2 and m3 are measures. Thebase data can include various information such as SKU, outlet, timeframe, sales, or in general any information that can be collected by theone or more information sources 20. This information can represent suchthings as products or services, and information related thereto such aslocation, quantity, sales, price, coupon usage, and in general anyinformation that is related to the product or service.

Next, in cooperation with the user interface module 190, the analysismodule 110 and the rules modules 120, one or more rule sets are selectedthat are used as a basis for tagging the base data with one or moreadditional dimension keys. Optionally, the data translation module 195can also convert the data as need to ensure inter-consistency andcompatibility with the rule set(s). The rule sets are used to identifyone or more features in the data and, if these feature(s) exist, thatrecord(s) is selected for subsequent tagging. For example, userinterface module 190 can provide a set of templates, which have acorresponding set of rules, which allow for the tagging of certaindimension keys that have a corresponding desired relationship that is tobe analyzed. For example, a “Seasonality Template” could be used fordetermining the sales of a product during a certain time frame, e.g.,fall. This seasonality dimension will have a corresponding rule set thatincludes, for example, product identifier, time frame, date of sale,retail outlet code, custom information or the like.

Therefore, when the seasonality template is selected via the userinterface module 190, the rules module, in conjunction with the datatagging module 130, analyzes the base data in conjunction with theanalysis module 110 and tags the additional dimension keys based on therule set(s). For example, the base data may have dimension keys d1, d2,d3 and the additional dimension keys can be e1, e2, . . . with the ruleset(s) that associate instances of d1 and d3 with instances of e1 andinstances of d2 and d3 with e2, resulting in a data structuredimensioned by d1, d2, d3, e1, e2.

The analysis module 110 then aggregates by the original keys (d1, d2,d3) and the new keys (e1, e2) with the resultant set being d1, d2, d3,e1, e2, m1, m2, m3.

The multidimensional cube 140 is then formed with dimensions (d1, d2,d3, e1, e2) and can be used for supplemental data analysis. For example,slice-and-dice analysis and consolidation can be performed through thevarious dimensions as if the dimensions were independent.

The plug-n-play module 180, in cooperation with the user interfacemodule 190, and the rules module 120, allows the selecting of variousdifferent dimensions as well as the ability to create new dimensions andcorresponding rules. For example, and in conjunction with the userinterface module 190, various different dimensions can be selectable bya user with, as each new dimension is selected, the process in FIG. 3implemented for analysis of the base data.

For example, various types of base data could have been collected.However, this base data may have not been collected to reflect aparticular event of interest. For example, an analysis may require areviewing of product performance, seasonality or promotioneffectiveness, or other criteria that may have not been recognizedduring the base data collection. In order to avoid reloading andreanalyzing of the data, the plug-n-play dimension module 180 allows foradditional dimensions to be analyzed with the additional dimension keysbased on the one or more rule sets being appended to the base data.

Having constructed the multidimensional cube 140, it is made availablefor slice-and-dice analysis, and the results, or portion thereof, can beused for one or more of feedback instructions for demand chainmanagement, for report generation that can be one or more of printedand/or displayed on printer 210, and stored in a data structure, such asdata structure 220. This data structure 220 could then itself be usedand fed-back into the system to, for example, add an additionaldimension or modify an existing dimension, and so on.

FIG. 2 illustrates an exemplary dimension link that illustrates therelationship between exemplary dimensions Product, Organization and Timeand a linked dimension such as Seasonality.

The dimension link connects Product at the SKU level, Organization atthe Location level, Time at the Day level to Seasonality at the Seasonlevel.

For example, suppose there is a Sales Fact Table in the Data Warehousewith sales measures dimensioned by Product at the SKU level,Organization at the Location level, Time at the Day level and a SeasonalPlan Fact Table also in the Data Warehouse with expense measuresdimensioned by Product at Class, Organization at Region, Seasonality atSeason level and Time at Week level.

For this exemplary embodiment, the data analysis system 1 can build anOLAP cube with the following dimensions: Product with levels Class andabove, Organization with levels Region and above, Seasonality withlevels Season and above, Time with levels Week and above. The ExpenseFact Table can be used as source data for such a cube. But by means ofthe dimension link, so can the Sales Fact Table. Through the dimensionlink sales data is available dimensioned by Product at SKU, Organizationat Location, Time at Day and Seasonality at Season. This can beaggregated to Product at Class, Organization at Location, Seasonality atSeason, Time at Week and hence is candidate source data for the cube.

FIG. 3 illustrates an exemplary methodology for linking discretedimensions according to this invention. In particular, control begins instep S100 and continues to step S110. In step S110, the base data iscollected in the form of d1, d2, d3, m1, m2, m3, wherein d1, d2, d3 aredimension keys and m1, m2, m3 are measures. Next, in step S120, the basedata is analyzed, one or more records meeting the criteria in therule(s) is selected and tagged with additional dimension keys, e.g., e1,e2, based on one or more rule sets, e.g., d1, d3, e1 and d2, d3, e2.Then, in step S130, aggregation is performed by the original keys (d1,d2, d3) and new keys (e1, e2) with the resultant set being in the formof d1, d2, d3, e1, e2, m1, m2, m3. Control then continues to step S140.

In step S140, a multidimensional cube is constructed with the dimensionsof d1, d2, d3, e1, e2 for data analysis. Next, in step S150,slicing-and-dicing can be performed. This slicing-and-dicing allowsanalysis and consolidation through the dimensions as though thedimensions were independent.

In step S160, a determination is made whether to add or modify adimension. If the desire is to add or modify a dimension, control jumpsback to step S120, with control otherwise continuing to step S170 wherethe control sequence ends.

FIG. 4 illustrates an exemplary mockup of “Seasonality” according tothis invention. The exemplary output grid 400 shows data for Sales,wherein “T” represents “no value.” The first column 410 indicates thechart is for a product called “Item 10000 1.” The second column 420differentiates Last Year Data (L Y), This Year Data (TY) and WorkingPlan Data. The third column 430 indicates Seasonality, in this exampleSpring, Fall and the Total of Spring and Fall (Ttl Seasonality). Theremaining columns 440 show the data for Weeks in March and April.

This particular product changes its Seasonality from Spring to Fall inApril 2009. The Last Year values are all in the Spring row because theproduct had Spring Seasonality last year. The This Year and Working Plan(the plan is for this year) values switch from Spring to Fall from thefirst week in April.

The exemplary methodology described herein is used to add theSeasonality dimension to the actual results (LY and TY)—this data havingbeen loaded without Seasonality as a dimension. Working Plan on theother hand is dimensioned by Seasonality. The result is the ability tocompare data with different original dimensions side by side.

A number of variations and modifications of the invention can be used.It would be possible to provide for some features of the inventionwithout providing others.

The exemplary systems and methods of this invention have been describedin relation to databases, data analysis and data structures. However, toavoid unnecessarily obscuring the present invention, the descriptionomits a number of known structures and devices. This omission is not tobe construed as a limitation of the scope of the claimed invention.Specific details are set forth to provide an understanding of thepresent invention. It should however be appreciated that the presentinvention may be practiced in a variety of ways beyond the specificdetails set forth herein.

Furthermore, while the exemplary embodiments illustrated herein showvarious components of the system collocated, certain components of thesystem can be located remotely, at distant portions of a distributednetwork 10, such as a LAN, cable network, and/or the Internet, or withina dedicated system. Thus, it should be appreciated that the componentsof the system can be combined into one or more devices, or collocated ona particular node of a distributed network, such as an analog and/ordigital communications network, a packet-switch network, acircuit-switched network or a cable network.

It will be appreciated from the preceding description, and for reasonsof computational efficiency, that the components of the system can bearranged at any location within a distributed network of componentswithout affecting the operation of the system. For example, the variouscomponents can be located in an analytical data tool and/or expert dataanalysis system.

Furthermore, it should be appreciated that the various links, such aslink 5, connecting the elements can be wired or wireless links, or anycombination thereof, or any other known or later developed element(s)that is capable of supplying and/or communicating data to and from theconnected elements. These wired or wireless links can also be securelinks and may be capable of communicating encrypted information.Transmission media used as links, for example, can be any suitablecarrier for electrical signals, including coaxial cables, copper wireand fiber optics, and may take the form of acoustic or light waves, suchas those generated during radio-wave and infra-red data communications.

Also, while the flowchart has been discussed and illustrated in relationto a particular sequence of events, it should be appreciated thatchanges, additions, and omissions to this sequence can occur withoutmaterially affecting the operation of the invention.

In yet another embodiment, the systems and methods of this invention canbe implemented in conjunction with a special purpose computer, aprogrammed microprocessor or microcontroller and peripheral integratedcircuit element(s), an ASIC or other integrated circuit, a digitalsignal processor, a hard-wired electronic or logic circuit such asdiscrete element circuit, a programmable logic device or gate array suchas PLD, PLA, FPGA, PAL, special purpose computer, any comparable means,or the like. In general, any device(s) or means capable of implementingthe methodology illustrated herein can be used to implement the variousaspects of this invention. Exemplary hardware that can be used for thepresent invention includes computers, enterprise systems, demand chainmanagement systems, handheld devices, and other hardware known in theart. Some of these devices include processors (e.g., a single ormultiple microprocessors), memory, nonvolatile storage, input devices,and output devices. Furthermore, alternative software implementationsincluding, but not limited to, distributed processing orcomponent/object distributed processing, parallel processing, or virtualmachine processing can also be constructed to implement the methodsdescribed herein.

In yet another embodiment, the disclosed methods may be readilyimplemented in conjunction with software using object or object-orientedsoftware development environments that provide portable source code thatcan be used on a variety of computer or workstation platforms.Alternatively, the disclosed system may be implemented partially orfully in hardware using standard logic circuits or VLSI design. Whethersoftware or hardware is used to implement the systems in accordance withthis invention is dependent on the speed and/or efficiency requirementsof the system, the particular function, and the particular software orhardware systems or microprocessor or microcomputer systems beingutilized.

In yet another embodiment, the disclosed methods may be partiallyimplemented in software that can be stored on a storage medium, executedon a programmed general-purpose computer with the cooperation of acontroller and memory, a special purpose computer, a microprocessor, orthe like. In these instances, the systems and methods of this inventioncan be implemented as program embedded on personal computer such as anapplet, JAVA® or CGI script, as a resource residing on a server orcomputer workstation, as a routine embedded in a dedicated measurementsystem, system component, or the like. The system can also beimplemented by physically incorporating the system and/or method into asoftware and/or hardware system.

The present invention, in various embodiments, configurations, andaspects, includes components, methods, processes, systems and/orapparatus substantially as depicted and described herein, includingvarious embodiments, subcombinations, and subsets thereof. Those ofskill in the art will understand how to make and use the presentinvention after understanding the present disclosure. The presentinvention, in various embodiments, configurations, and aspects, includesproviding devices and processes in the absence of items not depictedand/or described herein or in various embodiments, configurations, oraspects hereof, including in the absence of such items as may have beenused in previous devices or processes, e.g., for improving performance,achieving ease and/or reducing cost of implementation.

The foregoing discussion of the invention has been presented forpurposes of illustration and description. The foregoing is not intendedto limit the invention to the form or forms disclosed herein. In theforegoing Detailed Description for example, various features of theinvention are grouped together in one or more embodiments,configurations, or aspects for the purpose of streamlining thedisclosure. The features of the embodiments, configurations, or aspectsof the invention may be combined in alternate embodiments,configurations, or aspects other than those discussed above.

This method of disclosure is not to be interpreted as reflecting anintention that the claimed invention requires more features than areexpressly recited in each claim. Rather, as the following claimsreflect, inventive aspects lie in less than all features of a singleforegoing disclosed embodiment, configuration, or aspect. Thus, thefollowing claims are hereby incorporated into this Detailed Description,with each claim standing on its own as a separate exemplary embodimentof the invention.

Moreover, though the description of the invention has includeddescription of one or more embodiments, configurations, or aspects andcertain variations and modifications, other variations, combinations,and modifications are within the scope of the invention, e.g., as may bewithin the skill and knowledge of those in the art, after understandingthe present disclosure. It is intended to obtain rights which includealternative embodiments, configurations, or aspects to the extentpermitted, including alternate, interchangeable and/or equivalentstructures, functions, ranges or steps to those claimed, whether or notsuch alternate, interchangeable and/or equivalent structures, functions,ranges or steps are disclosed herein, and without intending to publiclydedicate any patentable subject matter.

What is claimed is:
 1. A method of linking one or more discrete dimensions through the use of a constructed multidimensional cube to improve performance of computer processing operations, the method comprising: collecting, by a computer, base data from one or more entities; storing, by the computer, the base data in a format comprising one or more dimension keys in a database; providing, by the computer, a set of templates that define one or more rule sets, the one or more rule sets identify criteria in the base data for subsequent tagging and associating an instance of at least one of the one or more dimension keys with an instance of at least one of one or more additional dimension keys; when base data is incompatible with the one or more rule sets defined by the set of templates, translating, by the computer, base data to a compatible data structure; comparing, by the computer, one or more records of the base data with the criteria of the one or more rule sets to determine whether the one or more records of the base data meet the criteria of the one or more rule sets, wherein meeting the criteria of the one or more rule sets triggers a tagging of the one or more records; tagging, by the computer, one or more records of the base data that meet the criteria of the one or more rule sets with one or more additional dimension keys; aggregating, by the computer, a single resultant set comprising all of the one or more dimension keys and all of the one or more additional dimension keys, the single resultant set aggregated without rebuilding the format comprising one or more dimension keys in the database; constructing a multidimensional cube with the one or more original dimension keys and the one or more additional dimension keys, thereby reducing computational run time of the computer; and storing the multidimensional cube in a storage device.
 2. The method of claim 1, further comprising adding another dimension key.
 3. The method of claim 1, further comprising modifying the one or more additional dimension keys.
 4. The method of claim 1, further comprising modifying the one or more original dimension keys.
 5. The method of claim 1, further comprising viewing the stored multidimensional cube from one or more different viewpoints.
 6. The method of claim 1, further comprising providing a user interface where one or more different dimensions can be selected.
 7. The method of claim 1, wherein a dimension is seasonality.
 8. The method of claim 1, wherein the base data includes information comprising one or more of SKU, outlet, time frame, sales, price and date.
 9. A non-transitory computer-readable storage media comprising processor executable instructions that when executed improves performance of computer processing operations and is configured to: collect base data from one or more entities; store the base data in a format comprising one or more dimension keys in a database; provide a set of templates that define one or more rule sets, the one or more rule sets identify criteria in the base data for subsequent tagging and associating an instance of at least one of the one or more dimension keys with an instance of at least one of one or more additional dimension keys; when base data is incompatible with the one or more rule sets defined by the set of templates, translate base data to a compatible data structure; compare one or more records of the base data with the criteria of the one or more rule sets to determine whether the one or more records of the base data meet the criteria of the one or more rule sets, wherein meeting the criteria of the one or more rule sets triggers a tagging of the one or more records; tag one or more records of the base data that meet the criteria of the one or more rule sets with one or more additional dimension keys; aggregate a single resultant set comprising all of the one or more dimension keys and all of the one or more additional dimension keys, the single resultant set aggregated without rebuilding the format comprising one or more dimension keys in the database; construct a multidimensional cube with the one or more original dimension keys and the one or more additional dimension keys, thereby reducing computational run time of the computer; and store the multidimensional cube in a storage device.
 10. The method of claim 1, further comprising determining and generating multiple different viewpoints based on a user request.
 11. A system of linking one or more discrete dimensions through the use of a constructed multidimensional cube to improve performance of computer processing operations, the system comprising: one or more computers comprising a processor and a memory, the one or more computers configured to: collect base data from one or more entities and stores the base data in a format comprising one or more dimension keys; provide a set of templates that define one or more rule sets, the one or more rule sets identify criteria in the base data for subsequent tagging and associating an instance of at least one of the one or more dimension keys with an instance of at least one of one or more additional dimension keys; when base data is incompatible with the one or more rule sets defined by the set of templates, translate base data to a compatible data structure; compare one or more records of the base data with the criteria of the one or more rule sets to determine whether the one or more records of the base data meet the criteria of the one or more rule sets, wherein meeting the criteria of the one or more rule sets triggers a tagging of the one or more records; tag one or more records of the base data that meet the criteria of the one or more rule sets with one or more additional dimension keys; aggregate a single resultant set comprising all of the one or more dimension keys and all of the one or more additional dimension keys, the single resultant set aggregated without rebuilding the stored base data format comprising one or more dimension keys; and construct and store in a storage device a multidimensional cube with the one or more original dimension keys and the one or more additional dimension keys, thereby reducing computational run time of the computer.
 12. The system of claim 11, further comprising a plug-n-play dimension module that allows adding dimension keys.
 13. The system of claim 11, wherein a plug-n-play dimension module allows modification of the one or more additional dimension keys.
 14. The system of claim 11, wherein a plug-n-play dimension module allows modification of the one or more original dimension keys.
 15. The system of claim 11, wherein the multidimensional cube can be viewed on a user interface from one or more different viewpoints.
 16. The system of claim 11, further comprising a user interface that allows different dimensions to be selected.
 17. The system of claim 11, wherein a dimension is seasonality.
 18. The method of claim 11, wherein the base data includes information comprising one or more of SKU, outlet, time frame, sales, price and date.
 19. A method of creating a data structure representing product or service information in the form of a cube having dimensions to improve performance of computer processing operations, the method comprising: receiving base data from one or more information sources; providing a set of templates that define one or more rule sets, the one or more rule sets identify criteria in the base data for subsequent tagging and associating an instance of at least one of the one or more dimension keys with an instance of at least one of one or more additional dimension keys; when base data is incompatible with the one or more rule sets defined by the set of templates, translating base data to a compatible data structure; comparing one or more records of the base data with the criteria of the one or more rule sets to determine whether the one or more records of the base data meet the criteria of the one or more rule sets, wherein meeting the criteria of the one or more rule sets triggers a tagging of the one or more records; tagging one or more records of the base data that meet the criteria of the one or more rule sets with one or more additional dimension keys; constructing and storing in memory a multidimensional cube including the one or more additional dimension keys, without rebuilding the information source, thereby reducing computational run time of the computer; and providing the multidimensional cube for slice-and-dice analysis. 